A global stereovision system combined with a local vision sensor is an effective approach to large-scale object measurement. However, obtaining the error distribution of such an approach remains a key research challenge in vision metrological applications. This paper investigates the calibration and the reconstruction uncertainty estimation method of the combined vision system. The measurement principle and the calibration method of the transformation matrix between the tracking-based measurement coordinate systems are presented. Furthermore, Monte Carlo simulation is utilized to determine the reconstruction uncertainty based on the theoretical measurement model and the experiment-based input uncertainty. The overall measurement uncertainty of the combined system is found to be 34.5% higher than that of the global vision system, which is more sensitive to the input pixel uncertainty than the local vision system. However, the combined vision system can achieve comparable measurement results within its larger working volume. This work contributes to a better understanding of the measurement uncertainty in combined tracking-based vision systems, as well as providing a few useful practice guidelines for using such a visual system.

结合局部视觉传感器的全局立体视觉系统是大规模物体测量的有效方法。然而，获得这种方法的误差分布仍然是视觉计量应用中的一个关键研究挑战。本文研究了组合视觉系统的标定和重建不确定度估计方法。介绍了基于跟踪的测量坐标系之间变换矩阵的测量原理和标定方法。此外，基于理论测量模型和基于实验的输入不确定性，利用蒙特卡罗模拟来确定重建不确定性。发现组合系统的整体测量不确定度比全局视觉系统高 34.5%，与局部视觉系统相比，它对输入像素的不确定性更敏感。然而，组合视觉系统可以在其更大的工作体积内实现可比的测量结果。这项工作有助于更好地理解基于组合跟踪的视觉系统中的测量不确定性，并为使用此类视觉系统提供一些有用的实践指南。